Penerapan Data Mining Untuk Prediksi Kelulusan Siswa Menggunakan Algoritma Naïve Bayes Pada SMK Garuda

  • Endah Yaodah Kodratillah Universitas Pelita Bangsa
  • Candra Naya Universitas Pelita Bangsa

Abstract

 Student graduation is a series of process stages that must be assessed by each student, including having to complete a predetermined number of lessons, and must meet the terms and conditions set by the school, because graduation is a reference so that they can continue their education to a higher level. and work in the business world and the industrial world. Therefore, this study will utilize data about student graduation by processing it using Data Mining to obtain information in the form of predictions of student graduation. The algorithm that will be used is the Naive Bayes algorithm. The attributes used in predicting student graduation are Practice Score, US (School Examination) and Student Behavior. Data Mining testing using the Naive Bayes algorithm produces an accuracy of 76.25%, precision 93.33%, recall 78.87% and an AUC value of 0.751. It can be concluded that the Naive Bayes algorithm has very good accuracy in this study.


Keywords: Data mining, Classification, C4.5 Algoritma, Naive Bayes

Published
2021-12-28
How to Cite
KODRATILLAH, Endah Yaodah; NAYA, Candra. Penerapan Data Mining Untuk Prediksi Kelulusan Siswa Menggunakan Algoritma Naïve Bayes Pada SMK Garuda. Jurnal SIGMA, [S.l.], v. 12, n. 4, p. 81 - 86, dec. 2021. ISSN 2407-3903. Available at: <https://jurnal.pelitabangsa.ac.id/index.php/sigma/article/view/974>. Date accessed: 27 june 2022.